Control of vehicle movement by application of geometric algebra and state and error estimation

ABSTRACT

A method and system for controlling movement of a vehicle. Movement, orientation, and position data of the vehicle is collected. A model of kinematics of the vehicle and its environment is created and a Theory of World model is produced and updated. The model includes geometric algebra multivectors. Errors and noise are stored as geometrically meaningful first-class objects within the multivectors. Geometric algebra operations are used to manipulate the model during operation. Error and noise data are propagated and manipulated using geometric algebra operations to reflect measurement and processing errors or noise. The models are used in generation of control data with a primary intent of ensuring stability. Operations such as intersections are used to compare position, orientation, and movement of the vehicle against position, orientation, and movement of objects in its environment. System tasks include, but are not limited to, kinematics, inverse kinematics, collision avoidance, and dynamics.

RELATED APPLICATIONS

This regular utility non-provisional patent application is acontinuation and claims priority benefit with regard to all commonsubject matter of earlier-filed non-provisional U.S. patent applicationSer. No. 16/219,609, filed on Dec. 13, 2018, and titled “CONTROL OFVEHICLE MOVEMENT BY APPLICATION OF GEOMETRIC ALGEBRA AND STATE AND ERRORESTIMATION”. application Ser. No. 16/219,609 claims priority benefitwith regard to all common subject matter of provisional U.S. PatentApplication Ser. No. 62/598,235, filed on Dec. 13, 2017, and titled“CONTROL OF VEHICLE MOVEMENT BY APPLICATION OF GEOMETRIC ALGEBRA ANDSTATE AND ERROR ESTIMATION”. The identified earlier-filed patentapplications are hereby incorporated by reference in their entiretiesinto the present application.

BACKGROUND

Control systems for vehicles are typically optimized considering theintended tasks of the vehicle as well as the environment in which thevehicle operates. In the past a driver or pilot controlled most, if notall, of the actuators that affect the movement of the vehicle. In thepast, some classes of vehicles began to have semi-autonomous orautonomous functions. Examples of such automation are auto-pilots onships and aircraft. Historically, many such systems have heavily reliedon inherent stability of mechanical design elements such as thestability of a fixed wing aircraft when a proper throttle setting hasbeen set by the pilot. In such cases, the control system will maintaincertain parameters within an envelope. This may mean adjusting controlsurfaces slightly to maintain parameters such as heading and altitude. Asimple Proportional-Integral-Derivative (PID) controller may besufficient to maintain the parameters. In such circumstances, the errorsignal can typically be contained only within the controller and willnot be shared with any external systems. A typical exception is that ifthe error exceeds a limit, then a failure indication may be shared withother systems. Even then the numeric error value likely will not beshared.

As technology has advanced, there are more systems being controlled thathave little or no inherent stability in their mechanical design.Examples include systems such as missiles, traditional rotorcraft, andmulti-copter designs. In some of these designs, the automaticallycontrolled actuators are completely necessary for any stability. Toexplain further, a multi-copter design stays in the air due to thethrust and lift produced by its rotors. Those rotors are also varied inspeed in order to control parameters such as attitude, altitude, speed,and translational movement. If the control system ceases function, thenthe multi-copter will immediately lose control and will fall. In such asituation the only forces acting on the multi-copter are gravity anddrag.

Control systems currently exist for the vehicles of all of these types,but their mathematical models are commonly limited in various ways. Oneproblem is that they may not maintain a consistent error or noise modelthroughout the control system. This means that there may be no estimateof the error and therefore the system may not even be able to indicatethat it has failed. Furthermore, they may have no means of combiningerrors from different sources or to accumulate error over time. They maynot be able to make decisions whether a given path is a safe path totake due to limited error estimation, if any.

Multi-copters which are commonly unmanned air vehicles, also called“drones,” are limited in many commercial applications by inherent flawsin existing technology, primarily flight control software intended tostabilize the drone and make it respond to flight commands. This can bedue to very limited mathematical models of the drone and itsenvironment. These vehicles may not maintain a consistent error or noisemodel throughout the control system. This means that there may be noestimate of the error and therefore the system may not be able toindicate that it has failed. Furthermore, they may have no means ofcombining errors from different sources or to properly account forvariations in error over time. They may not be able to make decisionsabout whether a given path is a safe path to take based on errorinformation. Conventional drones do not exhibit stable flight where GPSsignals are compromised, including inside buildings, under bridges, orunderground. Vision systems and other sensors might be employed toaddress this instability but cannot be effectively integrated into droneflight given the current state of drone flight software and othertechnical limitations.

Moreover, conventional drones have a non-unified representation of spacewhich must consider six dimensions (x, y, z, roll, pitch, and yaw) ofthe drone frame of reference, six dimensions of the world frame ofreference, and similar sets of dimensions for each sensor or visionsystem frame of reference. Such a non-unified approach, even iftechnically achievable, is prone to problems due to its inherentcomplexity. Through conventional matrix manipulation, it is subject to“gimbal lock,” contains no inherent means for processing, representing,and propagating error and noise, and as such is far from optimal.

SUMMARY

Embodiments of the present invention include systems and methods forcontrol of vehicle movement, such as flight, through use of particularlyrepresented frames of reference, relationships of error and noise toreference frames and flight paths, and the use of proper filtering andmanipulation of signals, thus coherent integration of flight and sensordata from multiple visual viewpoints is enabled. Embodiments of thepresent invention generally relate to vehicles which are digitallycontrolled and have some capability of measuring their own movement,position, and orientation or attitude as well as having some capabilityof measuring position, orientation, and movement data of theirsurrounding environment and have the ability of producing andmanipulating a model using the sensor data along with a storedrepresentation of the vehicle. The vehicle may be an on-the-ground,in-air, on-water, underwater, under-ground, or in-space vehicle, device,or machine. The vehicle may have at least one driver or pilot on-boardor off-board or may have no driver or pilot at all. The vehicle may ormay not carry passengers. The vehicle may or may not carry a payload.Fields of usage of the vehicle or simulations thereof includescientific, business, commercial, government, medical, military, hobby,and entertainment.

In certain embodiments, through appropriate representation of referenceframes, a unified representation of space is realized. The unifiedreference frames operated upon with geometric algebra, along withfurther representations of error and noise, create a basis for a new andmore effective means for navigation, stability, and performance ofdesired flight outcomes.

In certain embodiments, the space in which a drone calculates is unifiedacross all sensors and systems, using geometric algebra operators. Thisallows the drone to have a consistent, local predictive model of theworld derived from various state estimations, which will be referred toherein as a “Theory of World.”

In certain embodiments, errors and state variables in multiple framesfrom various sensors are handled in a way that allows a kinematicmodeling of the vehicle. An example of this is the treatment of windcorrection and collisions, which can be distinguished between andresponded to differently. In conventional drone platforms, it is largelyimpossible to tell the difference between confronting a wall from windor air movement, so the only answer is to avoid walls or objects and toassume all resistance is air movement. In conventional drones, handlingwall confrontations and wind or air movement the same often leads tounintended consequences such as flipping upside down when confronting awall because of misinterpretation of walls as resistance from air flow.In the case of air movement in the absence of GPS, such as in a cave ormine, the “drift” of the air movement or even drift of the gyroscopicsensor can be integrated out from the user's control bias. Certainembodiments of the present invention address this via what is called“Stick Integration,” a subset of “Empathy Modeling,” which is computedfrom data available to the vehicle including, but not limited to, userinputs as another form of sensors. The reason this is called “EmpathyModeling” is that although the human pilot (whether on board or remote)may not understand all aspects of control of the device, the device isable to make use of some signals the pilot is giving without the pilotbeing aware that he or she is providing them.

An approach of the present invention is a single multi-dimensionalmodel—it maintains all reference frames at all times, which enablesvision and flight-integrated automated behaviors. The present inventionmodels various kinematic parameters of the vehicle as well as items inits environment using the multivectors of a geometric algebra. Specificparameters embedded in the multivectors can include, but are not limitedto, rotational velocity, orientation, acceleration, velocity, andposition. The specific method of encoding can vary depending on the typeof geometric algebra being used.

In certain embodiments, the present invention optimizes a particularsensor or actuator as late as possible in the processing chain, ratherthan trying to build a unified space from overly optimized, small,individual spaces. This invention represents a complete shift in how thesoftware and math is applied for flight control. It postpones evaluationof sensor meaning and informational context until that information isrequired. This is possible because the sensor data has numeric error ornoise information embedded with it in a geometrically meaningful manner.

Any operations possible using the given geometric algebra may be appliedto the multivectors. Operations such as reflections, dilations,rotations, translations, etc. can be used on the multivectors to reflectthe measurements gathered by the sensors. A simpler geometric algebramay be used if only a simple model is needed. A more advanced model withmore advanced operations can require a greater geometric algebra.

Certain embodiments therefore comprise elements of a) unifiedrepresentation, b) treatment of sensory signals, c) management of drift,and d) representation of error or noise.

This summary is not intended to identify essential features of thepresent invention, and is not intended to be used to limit the scope ofthe claims. These and other aspects of the present invention aredescribed below in greater detail.

DRAWINGS

Embodiments of the present invention are described in detail below withreference to the attached drawing figures, wherein:

FIG. 1 is a perspective view of a drone constructed in accordance withan embodiment of the invention;

FIG. 2 is a schematic diagram of a control system of the drone of FIG.1;

FIG. 3 is a perspective view of an error sphere and the components it isdefined by (center and radius);

FIG. 4 is a perspective view of drone motion in multiple angles andthrough complex translations;

FIG. 5 is a perspective view of drone flight through a path;

FIG. 6 is a perspective view of a Sphere intersecting a plane—acondition that can be detected;

FIG. 7 is a perspective view of two frames of reference that are atdifferent orientations and are translated relative to each other;

FIG. 8 is a flow diagram of a method of estimating orientation andposition of a drone and integrating associated error and noise inaccordance with another embodiment of the invention;

FIG. 9 is an extension of FIG. 8;

FIG. 10 is a flow diagram of a method of controlling a drone inaccordance with another embodiment of the invention;

FIG. 11 is a plan view of a drone surrounded by its physicalradius-based sphere and expanded spheres that include noise or error;

FIG. 12 is a plan view of sensors on a drone and the beams generated dueto their associated orientations;

FIG. 13 is a perspective view of a sidereal reference frame with theEarth and a vehicle of the present invention; and

FIG. 14 is a perspective view of a sphere traveling through a path ofcircles.

The figures are not intended to limit the present invention to thespecific embodiments they depict. The drawings are not necessarily toscale.

DETAILED DESCRIPTION

The following detailed description of embodiments of the inventionreferences the accompanying figures. The embodiments are intended todescribe aspects of the invention in sufficient detail to enable thosewith ordinary skill in the art to practice the invention. Otherembodiments may be utilized and changes may be made without departingfrom the scope of the claims. The following description is, therefore,not limiting. The scope of the present invention is defined only by theappended claims, along with the full scope of equivalents to which suchclaims are entitled.

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features referred to are includedin at least one embodiment of the invention. Separate references to “oneembodiment”, “an embodiment”, or “embodiments” in this description donot necessarily refer to the same embodiment and are not mutuallyexclusive unless so stated. Specifically, a feature, structure, act,etc. described in one embodiment may also be included in otherembodiments but is not necessarily included. Thus, particularconfigurations of the present invention can include a variety ofcombinations and/or integrations of the embodiments described herein.

Turning to the drawing figures, and initially FIGS. 1 and 2, certainembodiments of the present invention may be used for controlling anautonomous or semi-autonomous device 100. The autonomous orsemi-autonomous device 100 may be a drone, a robot, a vehicle, acomponent thereof, or any other actuatable machine or device. Theautonomous or semi-autonomous device 100 may include a chassis 102, apropulsion system 104, and a plurality of effectors.

The propulsion system 104 may be mounted to the chassis 102 forgenerating acceleration forces. The propulsion system 104 may includepropellers, rotors, rockets, jets, thrusters, compressed gas expulsionsystems, buoyancy systems, actuators, wheeled drive trains, engines,motors, and the like. The propulsion system 104 may be commanded oractivated by signals from a processor of the control system describedbelow.

The effectors 106 may be mounted on the chassis 102 or mounted on orincorporated into components of the propulsion system 104 for directingthe acceleration forces and/or for changing attitude or orientation ofthe autonomous or semi-autonomous device 100. The effectors 106 may becontrol surfaces, steering mechanisms, rudders, diving planes, ailerons,exhaust directors, rotors, rotating wings, and the like. The effectors106 may be controlled by or given mechanical power by motors andactuators, and the like. The effectors 106 may be commanded by signalsfrom the processor of the control system described below.

A control system 200 of autonomous or semi-autonomous device 100 willnow be described in detail. The control system 200 broadly comprises aplurality of sensors 202, a plurality of transceivers 204 and aprocessor 206.

The sensors 202 sense positions, movement, and/or acceleration of theautonomous or semi-autonomous device 100 and thus may be mounted on theautonomous or semi-autonomous device 100 or positioned near theautonomous or semi-autonomous device 100. The sensors 202 may be or mayinclude accelerometers, inertial measurement units (IMUs), motionsensors, proximity sensors, pressure sensors, cameras, gimbals, radardetectors, lidars, avionics, multi-axis gyroscopic chips, multi-axisaccelerometers, magnetometers, ultrasonic distance sensors or any othersuitable sensing devices.

The transceivers 204 send and receive wireless signals between thecontrol system 200 and external devices, sensors, computing systems,and/or other autonomous or semi-autonomous devices. Each transceiver 204may operate in any suitable frequency on the electromagnetic scale. Forexample, some of the transceivers 204 may be radio frequencytransceivers while other transceivers may be line of sight transceivers.At least one of the transceivers 204 may receive global positioningsystem (GPS) data.

The processor 206 interprets data from the sensors 202 and data from thetransceivers 204 and controls the autonomous or semi-autonomous device100 according to the interpreted data and other inputs via thepropulsion system 104 and/or the effectors 106, as described in moredetail below. The processor 206 may include a circuit board, memory, andother electronic components such as a display and inputs for receivingexternal commands and a transmitter for transmitting data and electronicinstructions. The processor 206 may be mounted in or on the autonomousor semi-autonomous device 100 or may be part of a remote controller orremote computing system in communication with the autonomous orsemi-autonomous device 100.

The processor 206 may implement aspects of the present invention withone or more computer programs stored in or on computer-readable mediaresiding on or accessible by the processor. Each computer programpreferably comprises an ordered listing of executable instructions forimplementing logical functions and controlling the autonomous orsemi-autonomous device 100 according to commands and other inputs. Eachcomputer program can be embodied in any non-transitory computer-readablemedium, such as a memory (described below), for use by or in connectionwith an instruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device, and execute the instructions.

The memory may be any computer-readable non-transitory medium that canstore the program for use by or in connection with the instructionexecution system, apparatus, or device. The computer-readable medium canbe, for example, but not limited to, an electronic, magnetic, optical,electro-magnetic, infrared, or semi-conductor system, apparatus, ordevice. More specific, although not inclusive, examples of thecomputer-readable medium would include the following: an electricalconnection having one or more wires, a portable computer diskette, arandom access memory (RAM), a read-only memory (ROM), an erasable,programmable, read-only memory (EPROM or Flash memory), an opticalfiber, and a portable compact disk read-only memory (CDROM).

Mathematical Constructs

Turning to FIGS. 3-7, certain embodiments of the present inventioninvolve modeling of the world around a vehicle such as autonomous orsemi-autonomous device 100. These models of data must be encoded andstored in some data structure. In the case of this system it is a formof geometric algebra. The exact geometric algebra to be chosen candepend on the requirements placed on the autonomous or semi-autonomousdevice 100. A variable in geometric algebra will be called amultivector. Typically, geometric algebras are described in terms of aClifford algebra. A Clifford Algebra is described using the symbol g andits signature: the number of positive dimensions, the number of negativedimensions, and the number of null dimensions. For example, if onlyorientation data is desired, it is possible to store the Theory of Worlddata in a

_(3,0,0) algebra, but that prevents position data from also being storedin the same multivector as the orientations. For more powerful datarepresentation, it is possible to use

_(4,0,0),

_(4,1,0),

_(3,0,1),

_(3,0,2), or algebras of even higher dimensionalities.

The features of geometric algebra contain an object called a rotor whichis a mathematical object that can be used to rotate other multivectorsin the space of the geometric algebra in a manner similar to rotationsin the complex plane. Throughout the remainder of this document, theword rotor will refer to these geometric algebra mathematical objectsexcept in the case of multi-rotor which is intended to describe avehicle with multiple mechanical rotors.

U.S. Pat. No. 6,853,964 describes the geometric algebra representationsof

_(4,1,0), which is also called Conformal Geometric Algebra (CGA). Allrotations, translations, mirrors, rotations, etc. can be modeled usingversors or rotors which are multivectors themselves. The versors orrotors can be multiplied with other versors or rotors to describe anoverall movement or can be used for unification of reference frames.

Each type of geometric algebra allows certain geometric objects to bedescribed using a multivector. As an example geometric algebra, CGAallows points, lines, planes, spheres, as well as other shapes to berepresented in multivector form.

To simply represent a shape of some sort and to move it around would notbe useful if there was no way to make use of the transformed shapes.Geometric algebra allows the extraction of the location and sizes ofthese various shapes and it also allows the determination ofinteractions between shapes. One such interaction is the intersection602 between two shapes 601 and 603.

Certain embodiments of the present invention present a system and methodof controlling vehicle movement in which measurements and state data ofthe vehicle—along with their associated noise and/or error—arerepresented as geometric algebra objects. Any mathematical operationthat can be used on geometric algebra objects can therefore also beapplied to the vehicle's measurements and state data. The incorporationof noise and/or error into the geometry provides several advantages thatwill be understood from the below discussions.

Unified Spatial Representation

Certain embodiments of the present invention keep a unified spatialrepresentation throughout. This is done using the features of geometricalgebra. Geometric algebra can model an autonomous or semi-autonomousdevice 100 in a manner that is coordinate free. This does not mean thatcoordinates are meaningless to the mathematics, but rather much of thesymbolic manipulation of the mathematics can be handled early on toactually simplify the math. Later specific numeric coordinates,translation values, rotation values, errors, or noise can be fed in tosolve a given problem. The following discussion considers the autonomousor semi-autonomous device 100 to be a drone, although other vehicles anddevices may be used as described above.

The rotors also allow for many different frames of reference to becomeunified into one spatial representation. For example, a single rotor inthis geometric algebra could be used to effectively shift from one frameof reference to another. In some geometric algebras, this rotor couldprovide the shift of both rotation and translation. The information fromone viewpoint 701 can always be transformed to any other viewpoint 702through the use of rotors. The dashed line 703 indicates the differencein viewpoints that the rotors enable.

Unlike an automobile or airplane, a drone can rapidly move using manydegrees of freedom at the same time as perceived by the user asdemonstrated in FIG. 4. To use gaming terminology, it can strafe rightby rolling 403, while pitching up and down 402, while yawing 404, whilemoving or translating through space 405. These movements will lead tomany rotations and translations in a short period of time. This canseverely impair an imaging system (e.g., processor 206) which standsindependently; it is likely to suffer motion blur and orientation lossunless it is perfectly gimbaled, and even then, it might not be able tokeep up. In a unified space, however, images can be mathematicallyprojected in its internal model using state data from theavionics—namely orientation and translational state data. This assiststhe imaging system because the data from the avionics (e.g., sensors202) indicates the characteristics of the movement which will result inmotion blur. Therefore, the imaging system can either ignore or correctmotion blur in some cases. Information from the imaging system is alsofed into the avionics (akin to an optical flow sensor) because it is allpart of the same Theory of World. This is information sharing betweenmultiple subsystems in a common data set. This also allows thecontrolling of a camera gimbal from the internal state to be done easilybecause the orientation information from the flight control system canbe used to control the motors of the gimbal.

Turning to FIGS. 11-14 Error! Reference source not found., and withreference to the previous figures, a key aspect of autonomous orsemi-autonomous device 1210 is that there are multiple sensors 1211-1219involved, all with their own orientation as indicated by the beams1201-1209. The sensors 1211-1219 can all be operating in differentorientations of their own. The rotors of the geometric algebra willrotate the view of the sensors 1211-1219 to the appropriate orientationin the model. These sensors can include, but are not limited to,multi-axis gyroscopic chips, multi-axis accelerometers, magnetometers,cameras, lidars, and ultrasonic distance sensors.

This geometric algebra technique allows for these many orientations andpositions to be unified into one view as referenced from the chassis ofthe autonomous or semi-autonomous device 1210 or any other referenceframe. In addition, the frame of reference (402, 403, 404) of thechassis can also be unified with the frame of reference of the world408.

Additionally, as shown in FIG. 13, this system can unify with a siderealreference frame 1301 of space in order to account for the rotation 1303of the earth 1302 about the earth's axis 1304 since that rotation 1303affects the overall rotation measured by the gyroscope. Although thereare at least three frames of reference, the sidereal reference frame1301, the earth's reference frame 1302, and the vehicle's referenceframe 1306, the rotors of geometric algebra unify all of them.

This approach is also important when multiple drones are used (seeClusters example below), since each has its own dynamic viewpoint of oneshared space. Again, by using a proper geometric space, one sharedTheory of World can be generated; the relationship between each vehicleand that world being a simple rotor.

The control space is also the same. Paths, orbits, or waypoints aremapped into the full geometry. For example, a GPS waypoint (latitude,longitude, and elevation) becomes either a point 304 or sphere 301 inthe model space, as a geometric object. Orbits or arcs 405 also havesimple geometric representations such as “tubes” 501 connecting thewaypoint spheres. These tubes represent paths in the world.

Since this is the same space as the photogrammetry being done by theimaging system, it is straightforward to look for any intersections 602which are likely to represent collision hazards. The geometric algebrahas techniques to check for such intersections. Many other interactionsare possible to check or measure in geometric algebra. For example, itis possible to check whether an object is on one side or the other sideof a plane. Through the wedge products of the geometric algebra and thedual operator it is possible to generate the objects from the minimalnumber of points which define the object. For example, a sphere can bedefined with any four points on its surface. Another method ofgenerating a sphere is to define it by its center location and itsradius.

Treatment of Sensory and other External Signals, Noise, and Error

Certain embodiments of the present invention use the same computationalspace everywhere, connecting all local viewpoints by rotors. Not onlycan objects be represented by the multivectors, but also noise can berepresented as well.

Errors and uncertainties also have geometric representations and can behandled dynamically. One way that noise or error can be integrated intothis model is to represent the noise or error with a sphere 303. Forexample, the center 304 of the sphere can be used to describe thelocation of the vehicle while the radius 301 represents the amount ofpositional noise or error associated with the drone. It is convenient toassign the radius of the sphere to be one standard deviation.

When transformed, the noise or error in the transform is included andthe resultant radius is either larger (more noise or error) or smaller(less noise or error). These error dimensions demonstrate how thedrone's Theory of World incorporates a concept of noise or error.

For example, information from the gyroscopes can be mapped into the samegeometric algebraic space used by an optical flow system, and viceversa. Information from the gyroscopes and the imaging system can beeasily fused in an information filter, such as a Kalman filter. If theautonomous or semi-autonomous device 100 has only these two sensors,then the orientation model—along with the full state vector, covariancematrix, and its history—would be the drone's Theory of World. However,it is possible, and often desirable, to incorporate data from many moresensors into the Theory of World (e.g. multiple cameras, lidars,ultrasonic sensors, etc.) each carrying their own error or noiseinformation.

Certain embodiments of the present invention maintain a fixed worldreference frame 408, a dynamic vehicle reference frame (402, 403, 404)along with multidimensional flight parameters, and the evolvingrelationship between the two. The incorporation of optical flowinformation in a system which already contains translation and rotationsensor data is performed through a modern sensor fusion technique.Parameters which are sensed more accurately, with less noise and lesserror, are given higher confidence. Parameters, noise, and error are allsaved over history and therefore parameterized over time. Also, acontinuously updated transfer in the frequency domain is developed,resulting in a frequency-based confidence profile for each sensorchannel. Furthermore, other special conditions (such as flightconditions of speed, acceleration, altitude, and temperature) over whicheach sensor might deliver different signal performance, are alsoquantified. Fusion then combines these data for use at all update ratesappropriate to each flight control need, providing the maximumconfidence available in each case.

The capability of modeling noise or error is particularly relevant ininspection applications, where it is important to get close enough tothe target without hitting it; winds, updrafts, or turbulence around abridge, for example, create a very dynamic uncertainty environment. Asphere 502 around the drone 503 represents its knowledge of where thedrone is, the size of the drone, and the noise or error which isestimated for the drone's position. A wide tube 501 represents the pathit should fly and remain within; this is a view of its representation ina mathematical expression.

The radius of the sphere 502 is the sum of two numbers. The first is theconstant physical radius 1102 of the drone (shown in FIG. 11 as vehicle1101). The second is the standard deviation of the noise or errorassociated with the position of the vehicle multiplied by a constantk_(sd). If the noise or error is estimated to be zero, the physicalradius component alone ensures that the size of the drone is accountedfor in collisions. When noise or error is greater than zero, the overallsphere 1103 provides an additional buffer zone to account for theuncertainty of location. The constant k_(sd) parameterizes how manystandard deviations of avoidance zone should be provided around thevehicle at all times. The higher that k_(sd) is, the less likely thedrone is to collide with another object. As the amount of noise varies,the overall sphere size changes. For example if the noise has grown, thesphere becomes larger (shown as sphere 1104).

One possible representation of a path in this present invention is aseries of Conformal Geometric Algebra circles 1402 which aresufficiently close to one another as to not allow an error sphere 1401to pass between them, as shown in FIG. 14. Should a simple intersectiontest fail between the drone and path, the drone can immediately executea safety behavior such as backing off from the intersection, re-routingthe path to avoid the intersection, returning to home, or landing withcaution.

Drift and Attention to Information Quality and Rate

For drifts, it is vital to correct the drone's estimate of the gravityvector. Since a true gravity vector cannot be acquired for any flyingvehicle from a purely inertial system—some external reference is alwaysneeded. A pressure sensor can provide a reasonably good measurement ofheight over medium timescales (approximately 10 sec). This is too slowfor stabilization loops running at greater than 100 Hz, but the drone'sTheory of World can find a delta between the measurement and ahigh-quality prediction. As long as the delta is due to an error inorientation, it can compute a low-frequency correction to the downvector and keep the drone stable indefinitely.

One way that certain embodiments of the present invention correct thegravity vector estimate is through “Stick Integration” which is a subsetof “Empathy Modeling”. The pilot of the vehicle can observe drifts inflight because the errors in orientation lead to a translationalmovement of the vehicle in space. The pilot's natural tendency is tocorrect this drift by commanding the drone to go the opposite directionfrom its drift. For example, if the drone begins to drift backwards, thepilot will push the translational stick forward. The drone's softwarewill detect that the pilot is giving it a correction and therefore willcorrect its estimation of the relationship between the attitude ororientation of the drone and the gravity vector. One manner in whichthis empathy is carried out is to apply all of the right stick movementsto the orientation rotor as if the right stick were an additionalgyroscopic sensor. This is demonstrated within the Estimation ofOrientation and Position section. Because these corrections from theright stick accumulate over time in the orientation rotor, this isreferred to as “Stick Integration”. Without the Kinematic Modelingtechniques, this stick integration would be largely impossible.

The kinematic models of this invention distinguish the differencesbetween forces such as wind and walls. The invention will continuouslyevaluate the kinematic motion of the vehicle in the context of the noiseor error of the state data, while tracking the thrust it is attemptingto apply via its motors. When the kinematic model indicates that thethrust from the vehicle can overcome external forces such as in windyenvironments, the control system will allow adjustments in flight tocompensate for and overcome those external forces. However, when thekinematic model indicates that the thrust is not overcoming the externalforces applied to the drone, it recognizes that the external force mustbe a wall. Therefore, it limits the thrust in order to maintainattitude, and thus the vehicle does not flip upside-down.

Optical flow and subsequent ego-motion determination may be applied tothe problem of gyroscopic drift to effect gyroscopic drift reduction indrone flight control and stabilization. Such correction must be appliedat the right point in the processing of the gyroscope information, in acoherent and integrated fashion. In particular, some aspects ofstability are maintained at a fairly high frequency and low latency,significantly higher/faster than frame rates of most cameras. Typicaloptical flow and ego-motion algorithms have a minimum of 1 frame oflatency, and often much more.

In certain embodiments of the present invention, with the advantage oftemporal parameterization of sensor data along with associated noise anderror as described above, multiple ego-motion estimates can be providedover a range of time and frequency on a continuous basis from both thegyroscope and optical flow. This enables gyroscope correction at ratesconsistent with fractions of frame times and consistent with gyroscoperead rates such as between 100 Hz and 200 Hz. Computation of opticalflow and ego-motion with less than a full frame of latency has moreerror than with a full frame or with multiple frames. The temporalnature of noise on both optical and gyroscope sensors, appropriate tothe nature of those sensors, all within the unified representation aspresented, makes such stability calculations easy to express, implement,test, and adjust for optimum real-time function.

This awareness of the spatiotemporal situation is required for highperformance stability and is also shared with higher level functions.Examples of such functions are reason, mission objectives, and moregeneral perception, which constitute machine awareness. Some of thesehigher-level functions operate at a much lower rate and requiresignificantly more intense processing.

Basic Geometric Algebra

The following describe some basic properties of geometric algebra.

Scalars are multiplicatively commutative with vectors and multivectors:

λa=aλ

The basis vectors are anticommutative with the wedge product (outerproduct):

aΛb=−bΛa

However, the basis vectors are associative and distributive with thewedge product:

(aΛb)Λc=aΛ(bΛc)

aΛ(b+c)=aΛb+aΛc

The vectors are commutative with the dot product:

a·b=b·a

The geometric product of vectors is made up of the dot product and wedge(outer) product:

ab=a·b+aΛb

The dot product and wedge product for vectors can be defined in terms ofthe geometric product:

a·b=½(ab+ba)

aΛb=½(ab−ba)

A reverse of a multivector is designated with a tilde over themultivector (e.g., {tilde over (R)}) or after parenthesis. The reverseoperation reverses the order of all of the outer products. For example,the following shows a reverse:

(5+e ² Λe ³)^(˜)=(5+e ³ Λe ²)=(5−e ² Λe ³)

This identity also holds true:

(

)={tilde over (R)} ₁ {tilde over (R)} ₂

It should be noted that these bivectors squared behave like imaginarynumbers:

(e ₁ Λe ₂)²=(e ₂ Λe ₃)²=(e ₃ Λe ₁)²=−1

Conformal Geometric Algebra

The Conformal Geometric Algebra described herein uses

_(4,1,0) algebra for illustrative purposes. Other Conformal GeometricAlgebra constructs may be used without limiting the scope of theinvention. The

_(4,1,0) algebra is made up of the following five orthogonal basisvectors e₁, e₂, e₃, e₊, and e₊. These basis vectors have the followingimportant properties:

e ₁ ² =e ₂ ² =e ₃ ² =e ₊ ²+1

e ⁻ ²=−1

The e₊ and e⁻ basis vectors can be combined in the following ways tomake null vectors:

n=e ⁻ +e ₊

n=½ (e ⁻ −e ₊)

The square of the null vectors is equal to 0:

n ²=(e ⁻ +e ₊)²=0

n ²=(½(e ⁻ −e ₊)²=0

In order to use Conformal Geometric Algebra, it must be possible to mapfrom 3D Euclidean space into the Conformal space. The following explainsthat mapping.

A point 304 in conformal space is defined by the following equationwhere x is the vector 302 in 3D space describing the location of thepoint 304 and X is the conformal representation of the point:

X=x+½x ² n+n

The vector 302 is in terms of the e₁ 305, e₂ 306, and e₃ 307 unit basisvectors.

A sphere 303 in conformal space is defined by the following equationwhere ρ is the radius 301 and X is the conformal point 304 at the centerof the sphere:

S=X−½ρ² n

Note that this indicates a point is equivalent to a sphere with zeroradius. As the coefficient of the n null vector decreases, the radius ofthe sphere increases.

A circle C in conformal space is defined by the following equation whereS₁ and S₂ are spheres that intersect at the circle:

C=S ₁ ΛS ₂

There are numerous operations that can be applied to the geometricobjects of this Conformal Geometric Algebra. A couple are highlightedhere. It is important to be aware that in Conformal Geometric Algebrathe concept of a rotor is used for both rotations and for translations.

To prepare for a rotation of an object by an angle of θ in the bivector{circumflex over (B)} plane about the origin, a rotor R is created:

$R = {e^{{- \hat{B}}{\theta/2}} = {{\cos\left( \frac{\theta}{2} \right)} - {\hat{B}{\sin\left( \frac{\theta}{2} \right)}}}}$

To then rotate the object S into S′ using the rotor R:

S′=RS{tilde over (R)}

To prepare for a translation of an object by a 3D vector a, thefollowing rotor R is created:

$R = {1 + \frac{na}{2}}$

To translate the object S into S′ using the rotor R:

S′=RS{tilde over (R)}

To reverse a rotation or translation of S′ back into S the equation maybe reorganized like this:

S={tilde over (R)}S′R

To create a combined rotor R_(combo) from multiple individual rotorssuch as R₁ followed by R₂ followed by R₃:

R _(combo)=R₃R₂R₁

This combined rotor is then used like the other rotors have been used:

S′=R _(combo) S{tilde over (R)} _(combo) =R ₃ R ₂ R ₁ S{tilde over (R)}₁ {tilde over (R)} ₂ {tilde over (R)} ₃

EXAMPLE Estimation of Orientation and Position

Turning to FIGS. 8 and 9, an orientation and position estimation of theautonomous or semi-autonomous device 100 including integration of theassociated noise or error, will now be described. Note that in all ofthe mathematical symbols below, subscripts are not intended to be usedas an index. They exist to distinguish between symbols. Whenever anassignment of a variable is occurring in the algorithmic description,then :=is used to explicitly indicate assignment rather than equality.For example, the following indicates that x is to be incremented by 1:

x:=x+1

These steps implement kinematic techniques in the Conformal GeometricAlgebra. This is a simple example where there are no additional sensorsbeyond a gyroscope and accelerometer except as noted.

Start with the autonomous or semi-autonomous device 100 restingmotionless at an arbitrary home point, as shown in block 800. Thiscorresponds to a zero position and zero velocity. It also ensures thatthe only acceleration sensed by the vehicle is due to gravity.

Set an initial minimum position error value: ϵ_(p)=ϵ_(p,min) (e.g.ϵ_(p)=ϵ_(p,min)=0), as shown in block 802. ϵ_(p) will be the radius of aConformal Geometric Algebra sphere in later steps.

Set an initial minimum velocity error value: ϵ_(v)=ϵ_(v,min) (e.g.ϵ_(v)=ϵ_(v,min)=0), as shown in block 804. ϵ_(v) will be the radius of aConformal Geometric Algebra sphere in later steps.

Using sensors such as the accelerometer, magnetometer, etc., determinethe orientation of the autonomous or semi-autonomous device 100 giventhat the autonomous or semi-autonomous device 100 is not moving andinitialize R_(o) based on that measurement, as shown in block 806. R_(o)should reflect the rotation of the vehicle from a flat orientation. Ifthe vehicle were perfectly placed flat so that gravity was straightdown, then R_(o) would equal one. This rotor will use a subspace of theGeometric algebra which includes the scalar and bivectors. In some casesa magnetometer could also be used in this step to set the heading aspectof R_(o). Without the information from a magnetometer or other sensordata, absolute heading cannot be known.

A scalar ϵ₀ will be initially set to zero or some other minimal value toindicate the estimated standard deviation in the noise of orientation,as shown in block 808. The ϵ₀ value summarizes the orientation error ornoise in all axes together. Its “units” are radians.

A sphere V with the sphere's center indicating velocity and its radiusρ_(v)=68 _(v) representing noise or error is initialized, as shown inblock 810. The center of V will be initialized to the initial velocityvector v₀ which will typically be 0. V₀ is the conformal space pointrepresenting the initial velocity.

$V_{0}:={v_{0} + {\frac{1}{2}v_{0}^{2}n} + \overset{\_}{n}}$$V:={{V_{0} - \frac{ɛ_{v}^{2}n}{2}} = {v_{0} + {\frac{1}{2}\left( {v_{0}^{2} - ɛ_{v}^{2}} \right)n} + \overset{\_}{n}}}$

A sphere P will represent the position of the drone, as shown in block812. The sphere's center indicates position and its radius indicateserror or noise in position. The conformal point P₀ is created using thevector p₀. The p₀ vector can be set to 0 to indicate an arbitraryinitial point in space or it could be any other encoded value. Theinitial radius of P will be ρ_(p)=ϵ_(p).

$P_{0}:={p_{0} + {\frac{1}{2}p_{0}^{2}n} + \overset{\_}{n}}$$P:={{P_{0} - \frac{ɛ_{p}^{2}n}{2}} = {p_{0} + {\frac{1}{2}\left( {p_{0}^{2} - ɛ_{p}^{2}} \right)n} + \overset{\_}{n}}}$

A new loop is started at block 900.

First, get a new IMU sample of both acceleration a_(v) and rotationalvelocity Ω_(v) (both in the vehicle reference frame), as shown in block902.

Generate, based on whatever data sources desired, a bivector {circumflexover (Ω)}_(e) which is an estimate of the rotation rate of spacerelative to earth—more specifically the rate of space relative to thehome point, as shown in block 904. Theoretically {circumflex over(Ω)}_(e) can remain constant since the home point is fixed relative tothe rotation of the world, but there may be cases when a correction isneeded such as an error in latitude or an error in heading. If{circumflex over (Ω)}_(e) is assumed constant, then it doesn't need tobe recalculated during every cycle of the loop. It should be noted thatthe magnitude of {circumflex over (Ω)}_(e) is global, only the directionof {circumflex over (Ω)}_(e) is uncertain.

Based on sensor data and/or state data, generate an estimated gravityvector ĝ in the world reference frame as located at the center of P, asshown in block 906. Once again, especially in flights over a relativelysmall area of the surface of the world, ĝ should be able to remainconstant. However, if the autonomous or semi-autonomous device 100travels far enough or enough error existed initially, then ĝ may need tobe adjusted. If ĝ is assumed to be constant, then it doesn't need to berecalculated during every cycle of the loop. It is clear that themagnitude of ĝ is nearly the same anywhere on the surface of the earth,but the direction of ĝ is what will primarily need adjustment.

The step in block 908 is based on this analysis: Rotor R_(do) is therotation change of the drone relative to earth. Rotor R_(de) is therotation of space relative to earth. Rotor R_(dg) is the rotation ofspace relative to the drone. An update of the total rotation of thedrone relative to earth must be:

R _(o) :=R _(do) R _(o)

With brief reference to FIG. 13, the rotation change of drone 1306relative to earth 1302 (R_(do)) is the same as the rotation of the drone1306 relative to space 1301 ({tilde over (R)}_(dg)) further rotated bythe rotation of space 1301 relative to earth 1302 (R_(de)). Thus:

R _(do) R _(de) {tilde over (R)} _(dg)

The component rotations are:

R _(de) =e ^(−{tilde over (Ω)}) ^(e) ^(Δt/2)

R _(dg) =e ^(−Ω) ^(v) ^(Δt/2)

Therefore:

R _(do) =e ^(−{circumflex over (Ω)}) ^(e) ^(Δt/2) ^(e) ^(Ω) ^(v) ^(Δt/2)

Update the orientation of the autonomous or semi-autonomous device 100,by composing the R_(o) rotor with the additional rotations, as shown inblock 908. This can be done with the following equation.

R _(o) :=e ^(−{circumflex over (Ω)}) ^(e) ^(Δt/2) ^(e) ^(Ω) ^(v) ^(Δt/2)R _(o)

For empathy modeling in the form of stick integration, the R_(o) rotormay be rotated additionally based on a rotation rate Ω_(s), which isderived from the position of the translation stick position in its twoaxes (q_(x) and q_(y)), and a constant k_(s) which acts as a scalingfactor. This allows error corrections that the pilot makes to beintegrated into the orientation over time.

Ω_(s) :=q _(x)(e ₃ Λe ₁)−q _(y)(e ₂ Λe ₃)

R _(o) :=e ^(−Ω) ^(s) ^(k) ^(s) ^(Δt/2) R _(o)

For each sample of gyroscopic data, there is an additional amount ofnoise added. It can be thought of as additional noise power oradditional noise energy being added to the total noise. The additionalnoise from the gyroscope will be called σ_(g) ². Therefore, the totalamount of noise in the orientation, ϵ_(o) ², is updated with thefollowing formula, as shown in block 910.

ϵ_(o) ²:=ϵ_(o) ²+σ_(g) ²

Vector δ_(v) is the change in velocity due to acceleration in thedevice's reference frame over the time period measured. In thisequation, it can be seen that ĝ is rotated into the vehicle frame ofreference.

δ_(v):=(a _(v) −R _(o) ĝ{circumflex over (R)} _(o))Δt

Create a new translation rotor in the vehicle's frame of reference thatwill be used to translate by δ_(v).

$T_{vv}:={1 + \frac{n\delta_{v}}{2}}$

Then create a T_(ve) rotor which is a rotation of the T_(vv) rotor intothe earth's frame of reference.

T_(ve) :={circumflex over (R)} _(o) T _(vv) R _(o)

Update the velocity sphere, which is in the earth's frame of reference,using the T_(ve) rotor, as shown in block 912.

V:=T _(ve) V{tilde over (T)} _(ve)

Scale V, if needed so that the coefficient of n is 1, as shown in block914. This ensures that the following steps which resize the sphere workcorrectly as they assume n has a coefficient of 1.

Update the error in the velocity due to the angular estimated error overthe time of this iteration of the loop, as shown in 916. In this case,it is known that 1 σ_(g) of noise is happening in a rotation manner.That noise is happening at a distance of δ_(v) from the previous centerof the sphere, so it adds the square of δ_(v)σ_(g) to the noise of thevelocity sphere.

$V:={V - \frac{\left( {\delta_{v}\sigma_{g}} \right)^{2}n}{2}}$

Update the error in the velocity due to the acceleration error overtime, as shown in block 918. Increase the size of the velocity sphere bythe additional velocity error σ_(v) ² for this time period based on thenoise model. Note that in this example the radius of the sphere onlygrows, but in other variations on the algorithm other sensors can helpstabilize or even reduce the radius of the sphere.

$V:={V - \frac{\sigma_{v}^{2}n}{2}}$

Determine the current velocity vector of the drone by determining thecenter of the velocity sphere, as shown in block 920.

$v:=\frac{- \left\langle {\overset{\_}{n}{Vn}} \right\rangle_{1}}{V \cdot n}$

Determine δ_(p) which is the change in position based on accelerationand the velocity, as shown in block 922. The δ_(p) vector is in theearth's frame of reference.

δ_(p):=½({tilde over (R)}_(o) a _(v) R _(o) −ĝ)(Δt)² +vΔt

Assign the T_(p) rotor based on this equation, as shown in block 924.

$T_{p}:={1 + \frac{n\delta_{p}}{2}}$

Update the position sphere, as shown in block 926.

P:=T _(p) P{tilde over (T)} _(p)

Scale P so that the coefficient of n is 1, as shown in block 928.

Increase the position error sphere radius by the square of σ_(p), asshown in block 930. σ_(p) is the additional noise in position periteration. σ_(p) is determined from the noise model of the system. Notethat in this example the radius of the sphere only grows, but in othervariations on the algorithm other sensors can help stabilize or evenreduce the radius of the sphere.

$P:={P - \frac{\sigma_{p}^{2}n}{2}}$

Calculate ϵ_(p) to determine the radius of the position error sphere:

ϵ_(p) :=√{square root over (P·P)}

A sphere to be used for interference checks called Z which is concentricwith P may be created. The radius of Z is the sum of r_(device) (theradius of the drone) and k_(sd) times δ_(p).

$Z:={P + \frac{\left( {ɛ_{p}^{2} - \left( {r_{device} + {k_{sd}ɛ_{p}}} \right)^{2}} \right)n}{2}}$

Intersection operations etc. may be used between V and velocity limitobjects to check that velocity limits haven't been exceeded, as shown inblock 932.

Intersection operations etc. may be used between Z and models of wallsand other objects to check for possible collisions of known modeledobjects, as shown in block 934.

If desired, ϵ₀ may be compared against a limit to ensure orientationerror is in an acceptable range.

If desired, it is also possible to determine the radius of V so that itmay be checked against limits.

Go back to start of loop per block 936.

EXAMPLE Clusters

In a cluster application, a number of drones may swarm over an area. Forexample, ten mapping drones may swarm over an urban area. The drones mayconstantly move to keep relatively even coverage over a relevant areawhile not hovering in their own or each other's jet wash. Each dronetransmits position information as well as identity information. Eachdrone operates in a self-centered fashion to produce the requiredapplication behavior in a distributed fashion. The requirement to movewhile keeping out of each other's jet wash is implemented by assuringthat the drones keep some distance from each other. To that end, asphere is mathematically modeled to act as an intended exclusion areanear the drone while a larger sphere is modeled to act as an intendedinclusion area in order to keep close enough to other drones to assuresufficient overlap in sensor coverage to accomplish the mappingobjective. Those spheres include some extra size to express error inposition as described above. Ground nodes with more computing power canassemble a ground model obtained form all the drones in the swarm usingconventional reconstruction software methods along with video to producea full remotely sensed map.

The computation space is unified within each drone and furthermore canbe unified in the ground node(s). As such, reasoning about the wholeswarm is simplified, has reduced latency, and can track uncertainties(errors).

System Control Description

Turning to FIG. 10, an exemplary method of controlling movement of theautonomous or semi-autonomous device 100 will now be described indetail. First, vehicle inertial data may be received or obtained fromthe an IMU, as shown in block 1000.

At least one of vehicle state data and vehicle control data may also bereceived via one of the transceivers 204 or an optical sensor (e.g., oneof sensors 202), as shown in block 1002. The vehicle state data mayinclude GPS data, optical-based data, or the like. The vehicle controldata may be in the form of input signals transmitted from a controlleroperated by a user. The input signals may include a correctioncomponent.

Dynamic parameters of the autonomous or semi-autonomous device 100 maythen be determined based on at least one of the vehicle inertia data,vehicle state data, and vehicle control data, as shown in block 1004.The dynamic parameters may include at least one of position,orientation, velocity, angular velocity, acceleration, rotationalacceleration, force, and torque of the autonomous or semi-autonomousdevice 100. If the GPS data or vehicle control data is incorrect,incomplete, or unavailable, a weight of dynamic parameters associatedwith the vehicle inertia data may be increased. More generally, a weightof dynamic parameters of at least one of the vehicle inertia data,optical-based data, GPS data, and vehicle control data/input signals maybe increased if one of the vehicle inertia data, optical-based data, GPSdata, and vehicle control data/input signals is incorrect, incomplete,or unavailable.

At least one of errors and noise associated with the dynamic parametersmay then be determined, as shown in block 1006. The errors may at leastpartially correspond to the correction component.

The dynamic parameters may then be incorporated into geometric algebramultivectors, as shown in block 1008. The multivectors and a pluralityof scalars may be encoded in dual form so as to yield a weighted sum fora moment of inertia of the autonomous or semi-autonomous device 100 suchthat rotations and translations of the autonomous or semi-autonomousdevice 100 are combined in a single variable. In one embodiment, themultivectors may not include all blades of the geometric algebra.

In one embodiment, a dynamic parameter of one of the multivectorsincludes orientation of the autonomous or semi-autonomous device 100 andat least one of an estimated error and an estimated noise of theorientation dynamic parameter. A dynamic parameter of another one of themultivectors includes angular velocity of the autonomous orsemi-autonomous device 100 and at least one of an estimated error and anestimated noise of the angular velocity dynamic parameter.

In another embodiment, a dynamic parameter of one of the multivectorsincludes position and orientation of the autonomous or semi-autonomousdevice 100 and at least one of estimated errors and estimated noise ofthe position and orientation dynamic parameter. A dynamic parameter ofanother one of the multivectors includes velocity and angular velocityof the autonomous or semi-autonomous device 100 and at least one ofestimated errors and estimated noise of the velocity and angularvelocity dynamic parameter.

At least one of the errors and noise may then be propagated into thegeometric algebra multivectors, as shown in block 1010.

A movement control decision may then be calculated via the geometricalgebra multivectors, as shown in block 1012.

A movement control instruction signal may then be generated based on themovement control decision, as shown in block 1014.

The autonomous or semi-autonomous device 100 may then be controlledaccording to the movement control decision, as shown in block 1016. Thatis, the movement control instruction signal may be sent to thepropulsion system 104 and/or the effectors 106 so as to affect themovement control decision.

Persistent errors associated with the dynamic parameters may then bedetermined, as shown in block 1018. The persistent errors may correspondto a persistent correction component.

The persistent errors may then be normalized into the dynamicparameters, as shown in block 1020.

The dynamic parameters including the normalized persistent errors maythen be incorporated into the geometric algebra multivectors so as toform refined geometric algebra multivectors, as shown in block 1022.

Additional movement control decisions may be calculated via the refinedgeometric algebra multivectors, as shown in block 1024.

An additional movement control instruction signals may be generatedbased on the additional movement control decision, as shown in block1026.

The autonomous or semi-autonomous device 100 may be controlled accordingto the additional movement control decisions, as shown in block 1028.

Although the invention has been described with reference to the one ormore embodiments illustrated in the figures, it is understood thatequivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described one or more embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following:

1. A method of controlling a vehicle having a chassis, an inertialmeasurement unit (IMU), a propulsion system, a transceiver, and aprocessor, the method comprising, via the processor: obtaining vehicleinertia data via the IMU; obtaining vehicle state data and vehiclecontrol data via the transceiver, the vehicle control data correspondingto pilot inputs; determining a control aspect of the vehicle controldata, the control aspect being derived from control signalscorresponding to the pilot inputs; determining dynamic parameters of thevehicle based on at least one of the vehicle inertia data, vehicle statedata, and vehicle control data; calculating a movement control decisionbased on the dynamic parameters and the control aspect; generating amovement control instruction signal based on the movement controldecision; and controlling movement of the vehicle by sending themovement control instruction signal to the propulsion system such thatthe propulsion system effects the movement control decision.
 2. Themethod of claim 1, further comprising the step of incorporating thedynamic parameters and the control aspect into geometric algebramultivectors and calculating the movement control decision via thegeometric algebra multivectors.
 3. The method of claim 2, furthercomprising the step of determining at least one of errors and noiseassociated with the vehicle inertia data and the vehicle state data andpropagating the at least one of errors and noise into the geometricalgebra multivectors.
 4. The method of claim 3, wherein the errors arepersistent errors.
 5. The method of claim 1, wherein the control aspectis a pilot-induced correction.
 6. The method of claim 5, wherein thepilot-induced correction counters an error in the dynamic parameters. 7.The method of claim 5, wherein the control aspect is a pilot-inducedright-stick correction.
 8. The method of claim 7, wherein thepilot-induced correction counters air movement drift.
 9. The method ofclaim 5, further comprising the step of incorporating the dynamicparameters and the pilot-induced correction into geometric algebramultivectors and calculating the movement control decision via thegeometric algebra multivectors.
 10. The method of claim 9, furthercomprising the step of determining at least one of errors and noiseassociated with the vehicle inertia data and the vehicle state data andpropagating the at least one of errors and noise into the geometricalgebra multivectors.
 11. A control system for a vehicle having apropulsion system, an inertial measurement unit (IMU), and a transceiverconfigured to receive at least one of vehicle state data and vehiclecontrol data, the vehicle control data corresponding to pilot inputs,the control system comprising: a processor configured to: obtain vehicleinertia data via the IMU; obtain the at least one of vehicle state dataand vehicle control data via the transceiver; determine a control aspectof the vehicle control data, the control aspect being derived fromcontrol signals corresponding to the pilot inputs; determine dynamicparameters of the vehicle based on at least one of the vehicle inertiadata, vehicle state data, and vehicle control data; calculate a movementcontrol decision based on the dynamic parameters and the control aspect;generate a movement control instruction signal based on the movementcontrol decision; and control movement of the vehicle by sending themovement control instruction signal to the propulsion system such thatthe propulsion system effects the movement control decision.
 12. Thesystem of claim 11, the processor being further configured toincorporate the dynamic parameters and the control aspect into geometricalgebra multivectors and calculate the movement control decision via thegeometric algebra multivectors.
 13. The system of claim 12, theprocessor being further configured to determine at least one of errorsand noise associated with the vehicle inertia data and the vehicle statedata and propagate the at least one of errors and noise into thegeometric algebra multivectors.
 14. The system of claim 11, wherein thecontrol aspect is a pilot-induced correction.
 15. The system of claim14, wherein the pilot-induced correction counters an error in thedynamic parameters.
 16. The system of claim 14, wherein the controlaspect is a pilot-induced right-stick correction.
 17. The system ofclaim 16, wherein the pilot-induced correction counters airmovement-induced drift.
 18. The system of claim 14, the processor beingfurther configured to incorporate the dynamic parameters and thepilot-induced correction into geometric algebra multivectors andcalculate the movement control decision via the geometric algebramultivectors.
 19. The system of claim 18, the processor being furtherconfigured to determine at least one of errors and noise associated withthe vehicle inertia data and the vehicle state data and propagate the atleast one of errors and noise into the geometric algebra multivectors.20. A vehicle comprising: a propulsion system for propelling thevehicle; an inertial measurement unit (IMU) configured to generatevehicle inertia data; a proximity sensor configured to detect objectsnear the vehicle; a camera configured to collect optical data near thevehicle; a transceiver configured to receive vehicle state data andvehicle control data, the vehicle control data corresponding to pilotinputs; and a processor configured to: obtain the vehicle inertia datavia the IMU, object proximity data via the proximity sensor, and theoptical data from the camera; receive vehicle state data and vehiclecontrol data via the transceiver; generate a single, consistent localpredictive model based on the vehicle inertia data, the object proximitydata, the optical data, the vehicle state data, and the vehicle controldata; determine a control aspect of the vehicle control data, thecontrol aspect being derived from control signals corresponding to thepilot inputs; determine dynamic parameters of the vehicle based on atleast one of the vehicle inertia data, vehicle state data, and vehiclecontrol data; incorporate the dynamic parameters and the control aspectinto geometric algebra multivectors; calculate a movement controldecision based on the dynamic parameters and the control aspect via thegeometric algebra multivectors; generate a movement control instructionsignal based on the movement control decision; and control movement ofthe vehicle by sending the movement control instruction signal to thepropulsion system such that the propulsion system effects the movementcontrol decision.
 21. The vehicle of claim 20, wherein the vehicle is anin-air vehicle.
 22. The vehicle of claim 20, wherein the vehicle is atleast one of an on-the-ground vehicle, an on-water vehicle, anunderwater vehicle, an under-ground vehicle, or an in-space vehicle. 23.The vehicle of claim 20, wherein the vehicle is remotely controlled byan off-board pilot.